The present invention relates generally to computerized business management systems. More particularly, the invention relates to an electronic commerce marketing system that automates and optimizes the behavior of inbound and outbound marketing campaigns on behalf of each of several different parties seeking to exchange valuable resources. The optimization takes places over time as the system learns from previous results allowing such campaigns to tune themselves. In one embodiment, the computer-implemented system employs an optimization engine utilizing genetic algorithmic and game theory techniques augmented by a prediction engine that uses holographic neural network techniques. Although the system is well adapted and described in the context of a single family residential real estate marketing system, it may be readily adapted to for use in other businesses, as well.
The real estate industry has traditionally relied heavily on social contact (word of mouth) and comparatively simple advertising campaigns, all relying heavily on the interpersonal skills of the real estate company's agents. Although computer technology has been used to automate the collection and distribution of real estate multiple listing information, very little else has been done to improve the information infrastructure of the real estate business as a whole. Other industries, primarily those involved in direct business-to-business marketing, have begun to use computer systems to automate the marketing process. However, existing marketing computer systems have been designed to facilitate complex, multi-person sales activity between manufacturers and their customers and do not work very well in consumer markets such as the real estate market. On the other hand, current consumer marketing systems such as contact management systems, telemarketing systems, and mail order processing systems do not begin to address the more sophisticated aspects of real estate marketing. In any event, current business-to-business marketing systems and consumer marketing systems all suffer from similar common weaknesses: they either present no means for improving efficiency over time, or they place the burden of improvement on user-supplied rules which require trial-and-error experiments and separate ad hoc analysis of the results of those experiments.
To illustrate, consider some of the issues faced by the residential single family home real estate brokerage firm. First, for most people, the home is the most substantial asset they will ever own. The decision to buy or sell a home is an emotional one and many people will understandably take a long time to mentally commit that they are ready to buy or sell. Then, even if their home buying or selling readiness is high, there is the issue of locating a suitable property, obtaining financing, finding a buyer for a person's existing property, and so forth. All of these emotionally fraught uncertainties make the real estate business highly labor intensive. It has heretofore been very costly and inefficient to locate ready buyers and sellers using conventional mailing lists and telemarketing campaigns. Simply stated, there has heretofore been no way to predict which members of the potential customer population represent good leads on which to follow up.
As noted, real estate brokerage companies have traditionally relied heavily on their real estate agents to meet the potential customer population face to face, to identify the best buyer and seller candidates. This presents a significant problem for brokerage firms that are trying to increase market share while protecting profit margins. In this labor-intensive industry, increasing market share has traditionally required increasing the number of agents. Increasing the number of agents does not translate to increased productivity and efficiency since after a point the best agents have already been taken; in fact, increasing the number of agents can actually reduce efficiency. What is needed is a means to increase the overall efficiency of the entire marketing process for the broker, the agents and the public.
The present invention applies a highly sophisticated, computer-implemented system to this consumer marketing problem. Used properly over time, the computer-implemented system can re-engineer much of the marketing side of the business. Although the presently preferred embodiment is adapted to the residential single family home aspect of the real estate marketing business, the system described herein can readily be adapted to other types of businesses, particularly those involved in consumer marketing with a sales force.
According to one aspect of the invention, a computer-implemented, self-optimizing marketing system is provided. A campaign engine selectively generates and stores a campaign population, that is, a plurality of individual marketing campaigns, each campaign characterized by a plurality of campaign attributes. Campaigns can be created on behalf of a broker (e.g. via a broker employee), a member of the public (e.g. actual or potential buyers and sellers of properties) or agents (i.e. real estate sales associates). Campaigns perform useful work on behalf of the sponsor, typically oriented to fulfilling desires to utilize, incorporate, exchange, access or monopolize resources in a more automated and efficient way than would be possible for the sponsor to do otherwise. By way of example, the campaign population might include inbound campaigns that manage the leads generated by members of the public calling a broker's hotline to learn about properties for sale in that market. The campaign population might also include outbound campaigns to manage the process of finding the optimum candidates to receive direct mail about a property or seminar. Each of the specific campaigns in the campaign population is differentiated from other campaigns by their respective campaign attributes. The system also includes a data structure for the storage and retrieval of various campaign data including; the campaign's sponsor, the desires of that sponsor in terms of specific resources, and rules or constraints (e.g. time, money) on how to optimally meet those desires. The marketing systems provides for a feedback loop between outbound campaigns and inbound campaigns such that the results of inbound campaigns can be used to monitor and improve the effectiveness of outbound campaigns.
The marketing system further includes a data store for storing a person population, property population, agent population and a broker population. Such populations may include both actual and potential members (i.e. actual customers and potential customers in the person population, actual agents and potential agents in the agent population, etc. The person population consists of individual members, each comprising stored data that represents a plurality of current customers, past customers and non-customers adding up to all members of a market's total adult population. The members of the person population are each characterized by a plurality of person attributes. In the presently preferred embodiment, the person attributes would include suitable identifying information, allowing those members of the person population to be contacted. In the preferred embodiment, the person attributes also include quantitative attributes that indicate each (non-customer) member's readiness to enter the market and the desires of that member regarding properties on the market, currently or in the future. Similarly, the agent population consists of individual members, each comprising a plurality of agents or potential agents who themselves are characterized by a plurality of agent attributes, including actual or predicted quantitative attributes regarding an agents sales productivity, areas of specialization and market areas served. The property population for a particular market is fully represented in the data store with plurality of actual properties and potential properties (e.g. sub-developments with unsold lots, etc). The property attribute data includes previous sales history, current sales status (from multiple listing service data, summary and detail of accumulated inquiries by potential customers regarding the property and (for properties not yet up for sale) calculated probability of that property coming into the market soon, as well as predicted days-on-market. The broker population consists of one or more brokers whose attribute data include compensation plans for agents, available services, fees and costs for those services, sets of roles for employees, defined trading areas and various marketing assets.
The marketing system further includes an optimization engine that accesses campaign data, person data, property data, agent data and broker data. The optimization engine utilizes a scoring process for ordering the members of a particular population. The scoring process employs an adaptive scoring algorithm that alters the scores based upon relations among at least some of the campaign attributes and person, property, broker, or agent population member attributes. Scores are used to compete for scarce resources by way of an exchange process that itself comprises an optimization engine. This competition creates the desired optimization for the campaign. By way of example, members of the public who call the broker's hotline are treated by the inbound hotline campaign as potential lead. These potential leads enter the exchange process with particular scores that are used to allow those potential leads to compete for slots in the following day's call queue for outbound telemarketing. A formal bidding process takes place whereby the call queue slots bid for the best leads using rules specified to the governing inbound and outbound campaigns by the user. In addition, the campaign includes a set of policies that ensures that the overall call queue is also optimized to a desired level not just individual slots (that is, the total set of filled slots in the call queue meets some objective function as well as the individual slots). Leads that do not make it for that day's call queue are recycled into the next day's competition by being allowed to re-enter the next day's exchange process. In this way, the optimization engine continuously tunes itself to better achieve the desired goals of the appropriate inbound and outbound marketing system campaigns.
The marketing system also includes a prediction engine that accesses the data structures to issue predictions based on historical data. The prediction engine can be used to complement the key values generated from user-specified rules. Continuing with the example begun in the previous paragraph, leads obtained via an inbound hotline campaign are assigned a home buying or selling readiness state using rules specified in the campaign by the user. The prediction engine also issues its own predicted home buying or selling Readiness State for each lead. As more and more historical data is developed, the predicted states become more accurate than the ones generated by the user's rules. The exchange process begins to weigh the predicted states as more important than the user-specified, rule-generated states. The system provides for additional tuning of marketing campaigns in ways that extend beyond the initial rules provided by users. Thus, campaigns actually evolve and become more effectiveness over time.
In another aspect of the invention, the computer-implemented real estate marketing system facilitates commerce between real estate agents, members of the public and one or more real estate brokers. The system includes a campaign engine for selectively generating a plurality of marketing campaigns characterized by a plurality of stored campaign attributes. The campaign engine has several interfaces by which different users may interact with the system and by which different kinds of information can be input to and read from the system. These interfaces include an agent interface by which real estate agents may specify rules to be reflected in at least one marketing campaign, and by which real estate agents may receive leads representing selected members of the public. The system also includes a broker interface by which a real estate broker may specify rules to be reflected in at least one marketing campaign and by which selected ones of the stored campaign attributes may be communicated to the broker. Finally, the system includes a public interface whereby potential or existing customers may create campaigns to manage services to meet their needs to buy or sell properties and obtain agents to represent them in these transactions. The same system unites and optimizes the needs of all three classes of participants via the campaign engine, the optimization engine and the prediction engine. The broker can create campaigns that interact with agents or members of the public or other brokers. Agents can create campaigns to interact with other agents or the public or one or more brokers. Members of the public can create campaigns to interact with each other or agents or brokers. The computerized marketing system can provide these capabilities within one market or across many markets.
In yet another aspect, the invention provides a customer acquisition and retention system (CARS) and an object-oriented business computer-implemented modeling framework that may be used to construct such system. The object-oriented framework implements a computer-implemented database model that is well suited to a variety of different platforms, including relational database platforms. The framework includes market agents that encapsulate a plurality of attributes and operations performed by legal entities (e.g. persons using the system) and computer agents. The framework also defines a property (e.g., real estate, goods) and service object model that encapsulates attributes and operations associated with properties and services administered by the system. The framework further defines a campaign object model that encapsulates attributes and operations associated with campaigns (e.g. marketing campaigns) that are orchestrated to achieve predetermined objectives. In addition to the above components, the framework also includes further object models to represent listings and deals involving the property or services administered by the system, and an object model to represent geographic and demographic information.
For a more complete understanding of the invention and its many advantages, refer to the following specification and to the accompanying drawings.